In recent years, researchers have recognized that watermarking can be viewed as communication with side-information at the transmitter, as first studied by C. E. Shannon and described in an article entitled “Channels with Side Information at the Transmitter,” IBM Journal of Research and Development, pages 289-293, 1958. This view of watermarking is illustrated in FIG. 1. The watermark embedding process is divided into two steps. In the first step a source message is encoded as a watermark pattern, w, and in the second step, the pattern is added to a cover Work, I, to produce the watermarked Work, I′. The encoding process is equivalent to a transmitter in a communication system. The addition of the cover Work is equivalent to a noise source in the transmission channel. The transmitter has access to this noise source (cover Work), which represents “side-information” that it may exploit during coding.
It shall be understood by those skilled in the art that the term “Work” as used herein includes any media object, such as an image, an audio clip, a three-dimensional model, and the like.
Research based on this side-information view of watermarking has followed at least two distinct paths. In the research path described in I. J. Cox et al., in an article entitled “Watermarking as Communications with Side Information,” Proceedings of the IEEE, 87(7), pages 1127-1141, 1999 and M. L. Miller et al., in an article entitled “Informed Embedding: Exploiting Image and Detector Information During Watermark Insertion,” IEEE International Conference on Image Processing, September 2000, novel embedding algorithms use the side information to improve the robustness of watermarks that will be detected by conventional, normalized-correlation detectors. The principal justification for these detectors is that they are immune to changes in the amplitude of the watermarked Work, such as occur when audio volume is turned down, or image contrast is adjusted.
The other research path as described by B. Chen et al. in an article entitled “Dither Modulation: a New Approach to Digital Watermarking and Information Embedding,” Proceedings of SPIE, Security and Watermarking of Multimedia Contents, volume 3657, January 1999; J. Chou et al. in an article entitled “On the Duality Between Distributed Source Coding and Data Hiding,” Proceedings of Asilomar Conference, November 1999; M. Ramkumar in article entitled “Data Hiding in Multimedia: Theory and Applications” Ph.D. Thesis, New Jersey Institute of Technology, Kearny, N.J., USA, November 1999; and J. J. Eggers et al. in an article entitled “A Blind Watermarking Scheme Based on Structured Codebooks,” IEE Seminar, Secure Images and Image Authentication, pages 4/1-4/6, 2000 introduces novel embedders and detectors that significantly increase the data payload of robust watermarks. A central characteristic of these systems is the use of multiple, alternative encodings for each possible message. However, most of these designs ignore the problem of changes in amplitude.
In most early watermark embedding algorithms, such as those described in I. J. Cox et al, “A Review of Watermarking and the Importance of Perceptual Modeling,” Proceedings of SPIE: Human Vision and Electronic Imaging II, volume 3016, pages 92-99, 1997, the cover Work was ignored during the process of encoding a message. Later algorithms used perceptual modeling to adaptively attenuate the watermark pattern, making it less perceptible, but still ignored the position of the Work relative to the detection region in media space. When the resulting watermark patterns were added to the original Work, the patterns would not necessarily produce watermarked images inside the detection region.
The two paths of research described above that form the background of the present invention both stem from the realization that the cover Work need not be ignored during coding.
Once the encoder need not ignore the cover Work, it is possible to have complete control over the embedder output, since the encoder can set the watermark pattern to the difference between a desired output watermarked Work I′ and the original cover Work I, w=I′−I. If the detector is fixed, then the problem of embedding becomes one of choosing the best point, I, within the detection region for the desired message.
In Miller et al. supra, the detector was assumed to employ normalized-correlation or correlation-coefficient as a detection metric. These closely-related metrics are commonly used in watermark detection because the normalization step makes them independent of scaling, such as occurs when image contrast is adjusted.
Under the assumption of a normalized-correlation detector, Miller et al. supra explored four different strategies for choosing I′. The two strategies with the best performance involved a robustness measure, r2, first described in Cox et al. supra. This value estimates the amount of I.I.D (independent, identically distributed) Gaussian noise that may be added to the watermarked image, I, before it is likely to go outside the detection region. The value r2 is given by             r      2        =                  (                              v            ·            w                                T            ⁢                                        w                                                    )            -              v        ·        v              ,where v is a vector extracted from a watermarked Work, w is a watermark pattern, and T is a constant. One strategy was to maximize r2 while maintaining a constant distance (fidelity) between I and I′. The other strategy was to minimize the distance between I and I′ while maintaining a constant value of r2.
The strategy of keeping r2 constant produced the most consistently robust watermarks. However, this consistent robustness came at a cost of inconsistent fidelity.
Most of the research based on the side-information view of watermarking has concentrated on increasing data payload. This was inspired by the article by M. Costa, “Writing on Dirty Paper,” IEEE Transactions on Information Theory, Vol. 29, No. 3, pages 439-441, May 1983, studied a channel like that shown in FIG. 1. He found that, if the two noise sources (cover images I and processing noise n, in the case of watermarking) are both I.I.D. Gaussian, and the transmitted signal, w, is limited by a power constraint, then the capacity of the channel is dependent only on the second noise source. Since the data payload of most proposed watermarking systems is severely limited by interference from cover images, Costa's results implies that their performance can be greatly improved upon.
Costa showed that the capacity of his communications channel could be approached using what can be called a one-to-many or dirty-paper code. In such a code, there is a one-to-many mapping between messages and code vectors. From amongst the many vectors that encode a single, desired message, the encoder selects a vector, u, based on the value that will be added from the first noise source, I. In Costa's construction, the encoder transmits w=u−αI, where α is a constant between 0 and 1. To decode a received signal, the receiver finds the closest code vector, u, and identifies the message to which it corresponds. With a properly-designed code book, and a carefully selected value of α, w is virtually guaranteed to satisfy the power constraint, and the code rate can approach channel capacity.
Realizing Costa's result in practice presents a major problem. If one wishes to have data payloads in the hundreds or thousands of bits, the size of the code is far too large to be implemented with brute-force searches. Some form of structured code must be used that allows the closest code vector to a given vector to be found efficiently. Several researchers have suggested using lattice codes, with each message represented by a sub-lattice. Chou et al. supra describe an alternative method based on the syndrome coding ideas of Pradhan et al., “Distributed Source Coding Using Syndromes: Design and Construction,” Proceedings of the Data Compression Conference (DCC), March 1999.
In principle, an increase in data payload can be traded off for an increase in robustness, and vice-versa. Consider the following four properties of a watermark:                Robustness (ability to survive normal processing)        Fidelity (perceptual distance between original and watermarked version of a image)        False positive probability (probability that a watermark will be detected in an unwatermarked image)        Data payload (log2 of the number of distinct watermark messages that can be embedded and detected)        
In general, it is possible to trade any of these properties for any other. For example, suppose there is a system that detects a watermark in every image, so its false positive probability is 100%, but the watermarks have a high data payload, say 1024 bits. In such a system, it is possible to improve the false positive rate at the expense of data payload by specifying that the last 24 bits of every message must be 1's—a message with any other combination of final bits indicates that no watermark is present. If, in an unwatermarked image, each bit has an even probability of being a 1 or a 0, and the bits are independent, then this gives a false positive probability of 2−24, with a data payload of 1000 bits.
In principle, it is possible to also trade false positive probability to gain data payload. Consider a system that has 0 bits of data payload, in the sense that there is only one message that may be embedded, and the detector simply reports whether the watermark is present or not. Suppose the detection region covers ½24th of the distribution of unwatermarked images, giving a false positive probability of 2−24. In theory, it is possible to define 224 similarly-shaped, non-overlapping detection regions to obtain a watermarking system with 24 bits of data payload but 100% false positive probability.
Similarly, false positive probability can be traded for robustness, by increasing the size of the detection region. With a normalized-correlation detector, this is a simple matter of lowering the detection threshold. And the robustness and fidelity can also be traded for one another, by varying how far the embedder moves Works into the detection region.
Thus, an improvement in any one of these four areas can, in principle, translate into an improvement in one of the others. Specifically, if an existing watermarking system is modified to exploit an idea designed to improve data payload, but to hold the data payload constant, it should be expected that one or more of the other properties improves.
The key idea behind Costa's result is the use of a one-to-many code. Costa's actual codes and communications algorithms are designed for a channel in which both noise sources are I.I.D. Gaussian, which is not the case in most watermarking systems.
Each cover Work I may be considered as a point in a K-dimensional media space. With respect to a given watermark pattern w, the watermark detector defines a detection region, which is the set of all points that the detector will categorize as containing that message. When a watermark embedder embeds a source message in a cover Work, the embedder attempts to find a new Work that is perceptually similar to the cover Work, but that lies within the detection region for the source message.
A novel aspect of the present invention is that a source message can be represented by a quantity of alternative watermark patterns. The embedder embeds whichever of these patterns most closely resembles the cover Work so that the most relevant watermark message is used. The detector reports that the source message was embedded if any one of the alternative watermark patterns is detected.
The present invention combines ideas from the two research paths described above in a simple image watermarking system. Specifically, the systems examined here are based on one described in Miller et al. supra, which uses normalized correlation in the detector, and employs side information to improve robustness.
A principal object of the present invention is therefore, the provision of a watermarking method where the encoding of the watermark pattern into a cover Work uses a one-to-many or dirty-paper code for the watermark pattern such that a single source message can be represented by a plurality of watermark patterns.
Another object of the present invention is the provision of a watermarking method where “side-information” is used in the embedding process.
A further object of the present invention is the provision of a watermarking method wherein the decoding of the watermark relies upon correlation coefficients.
Further and still other objects of the present invention will be more clearly understood when the following description is read in conjunction with the accompanying drawings.